Computer and data center load determination

ABSTRACT

A method for use in deploying computers into a data center includes calculating in a computer an expected peak power draw for a plurality of computers. The expected peak power draw for the plurality of computers is less than a sum of individual expected peak power draws for each computer from the plurality of computers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.14/094,464, entitled “COMPUTER AND DATA CENTER LOAD DETERMINATION,” andfiled Dec. 2, 2013, which is a divisional of U.S. patent applicationSer. No. 12/134,509, entitled “COMPUTER AND DATA CENTER LOADDETERMINATION,” and filed Jun. 6, 2008, now U.S. Pat. No. 8,601,287,which claims priority to U.S. Provisional Application Ser. No.60/976,386, filed on Sep. 28, 2007, and U.S. Provisional ApplicationSer. No. 60/942,963 filed on Jun. 8, 2007. The entire contents of allprevious applications are hereby incorporated by reference.

TECHNICAL FIELD

This document relates to data centers, and the design, provisioning andcontrol of data centers.

BACKGROUND

Higher speed computers come with a cost—higher electrical consumption.For a small number of home PCs this extra power may be negligible whencompared to the cost of running other electrical appliances in ahousehold. However, in data center applications, where thousands or tensof thousands of microprocessors may be operated, electrical powerconsumption becomes important.

In addition, the power consumed by a microprocessor is transformed intoheat. A pair of microprocessors mounted on a single motherboard can draw200-400 watts or more of power. If that power draw is multiplied byseveral thousand (or tens of thousands) to account for the computers ina data center, the potential for heat generation can be appreciated.Thus, not only must a data center operator pay for electricity tooperate the computers, it must also pay to cool the computers. The costof removing heat may be a major cost of operating large data centers.

Large-scale Internet services require a computing infrastructure thatcan be described as a warehouse-sized computing system. The cost ofbuilding data center facilities having the capacity to deliver the powerrequired by such a computing system can rival the recurring powerconsumption costs themselves.

SUMMARY

In one aspect, a data center includes a power distribution networkhaving a power capacity, and a plurality of computers drawing power fromthe power distribution network. Each of the computers has a peak powerdraw. The power capacity is less than a maximum power draw defined bysumming the peak power draw from each of the plurality of computers.

Implementations may include one or more of the following. The peak powerdraw of a computer may be a power draw under a maximum utilization of acentral processing unit of the computer. Each of the plurality ofcomputers may run an application, and the peak power draw of a computermay be a maximum power draw exhibited by the computer while running theapplication. Different computers in the plurality of computers may rundifferent applications, or may have different peak power draws. Theplurality of computers may include at least 1000 computers, and themaximum power draw may be more than 5% greater than the power capacity.The plurality of computers may include at least 5000 computers, and themaximum power draw can be more than 7% greater than the power capacity,e.g, about 40% greater than the power capacity.

In another aspect, a method of designing a data center includesdetermining a design power density, determining an oversubscriptionratio, and determining a spatial layout of the data center using thedesign power density and the oversubscription ratio.

Implementations may include one or more of the following. The facilitymay be constructed having the spatial layout. Determining the spatiallayout may include determining a total length of one or more rows ofracks of computers. Determining the total length may include dividing apower budget by the design power density and multiplying by theoversubscription ratio. Determining an oversubscription ratio mayinclude calculating a ratio of peak power draw of a computer to peakpower draw per computer of a plurality of computers.

In another aspect, a method of designing a data center includesdetermining a design power density, the design power density being inunits of power per unit length.

Implementations may include one or more of the following. The facilitymay be constructed having a power carrying-capability meeting the designpower density.

In another aspect, a method of deploying computers into a data centerincludes calculating in a computer an expected peak power draw for aplurality of computers.

Implementations may include one or more of the following. Calculatingthe expected power draw may include extrapolating a power draw for Ncomputers from a measured power draw of M computers, wherein N isgreater than 500 and M is less than 80. M may be 1, and N may be greaterthan 5000. Extrapolating may include extrapolation based on measurementsof power draw by N or greater and M or less computers under a differentoperating condition than the computers for which the expected power drawis being calculated. The operating condition may be computerconfiguration or an application being performed by the computer.

In another aspect, a method of monitoring power load of a data centerincludes storing data representing peak power usage of a plurality ofcomputers in the data center, collecting CPU utilization for at least astatistical sample of the plurality of computers, and calculating apower load of the plurality of computers from the CPU utilization andthe peak power usage.

In another aspect, a method of monitoring power load of a data centerincludes measuring a power usage of a cluster of the data center,measuring a power usage of a power distribution unit (PDU) of the datacenter, and measuring a power usage of at least one of a rack or acomputer within a rack.

In another aspect, a method of monitoring power load of a data centerincludes determining a power usage for each cluster, each powerdistribution unit and each rack, and comparing the power usage to astored maximum power capacity of the cluster, power distribution unit orrack.

Implementations may include one or more of the following. Performance ofa plurality of computers may be adjusted if a determined power usage iswithin a threshold of the maximum power capacity. Adjusting performancemay includes adjusting job allocation, adjusting job scheduling,adjusting a central processing unit execution frequency, or shuttingdown servers. Determining may include calculating the power usage fromprocessor and/or other component utilization, or measuring the powerusage.

In another aspect, a method of controlling power usage in a data centerincludes generating a signal indicating that a power usage is within athreshold of a maximum power capacity, and in response to said signal,adjusting performance of a computer.

Implementations may include one or more of the following. Adjustingperformance may include one or more of adjusting job scheduling oradjusting a central processing unit execution frequency.

In another aspect, a method of estimating peak power usage of acomputing system having a CPU includes determining the components in thecomputing system, determining a peak power usage value for eachcomponent, and adding the peak power usage value for each componenttogether to result in an actual peak power value.

In another aspect, a method of modeling computer system power usage fora single system includes running a group of computing systems, whereineach computing system has a CPU, measuring total system power usage ofthe computing system, measuring CPU utilization of each computing systemin the group, and determining a line fit to the system power to CPUutilization to create the model.

In another aspect, a method of determining a number of computing systemsthat can be run in a data center includes determining a total poweravailable, and selecting a number of computing systems based on themethod of modeling and an estimated amount of CPU utilization over agiven period.

In another aspect, a method of running multiple computing systems tomaximize a number of systems in use with the amount of power availableincludes selecting a first workload type, a second workload type and athird workload type, wherein the first workload type is a service with ahigh request throughput and large data processing requirements for eachrequest, the second workload type is an Internet service and the thirdworkload type is a service that runs offline batch jobs, determining apower usage for each of the first workload type, second workload typeand third workload type, selecting a highest power usage value of thefirst workload type, second workload type and third workload type, andassigning a number of systems to run the first workload type, secondworkload type and third workload type based on the amount of poweravailable and the highest power usage value.

In another aspect, a method of safeguarding multiple computing systemsin a datacenter includes selecting a value below an actual peak power,monitoring power usage of the multiple computing system during use,predicting future power usage based on requested tasks, determiningwhether the future power usage will exceed the value, and if the futurepower usage will exceed the value, implementing component-level powermanagement control functions or descheduling tasks.

In another aspect, a method of modeling to simulate potential for powerand energy savings in a datacenter includes selecting a threshold CPUutilization rate, and for each computing machine having a CPU componentin a group of computing machines that falls below the threshold CPUutilization rate, reducing the CPU component of total power and leavingpower consumption of remaining components of the computing machineunchanged.

In another aspect, a method of modeling non-peak power efficiencyincludes setting an idle power of each machine in a group of machines atabout 10% of actual peak power, and corresponding power usage greaterthan idle as proportional to increased activity.

Advantages can include one or more of the following. Additional computerequipment can be deployed in a data center within the same power budgetwith little risk of exceeding the power budget. Cost for excess powercapacity of the power distribution equipment can be reduced. The risk ofexceeding power budget can be evaluated before upgrading a data centerfacility with new equipment.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

DESCRIPTION OF DRAWINGS

FIGS. 1A and 1B are sectional side and plan views, respectively, of afacility operating as a data center.

FIG. 2 is a simplified schematic of a datacenter power distributionhierarchy.

FIG. 3 is flowchart illustrating a method of power planning.

FIG. 4 is a schematic illustration of a graphical user interface frompower usage calculation software.

FIG. 5 is a simplified datacenter power distribution hierarchy.

FIG. 6 is a graph of a model fitting system power to CPU utilization atthe machine level.

FIG. 7 is a graph of a modeled power versus measured power at the powerdistribution units level.

FIG. 8 is a schematic illustration of the collection, storage andanalysis architecture.

FIG. 9A is a graph of a cumulative distribution function of power usagenormalized to actual peak for Websearch, and FIG. 9B is an expanded viewof a portion of the graph (power from FIG. 9A.

FIG. 10A is a graph of a cumulative distribution function of power usagenormalized to actual peak for Webmail, and FIG. 10B is an expanded viewof a portion of the graph from FIG. 10A.

FIG. 11A is a graph of a cumulative distribution function of power usagenormalized to actual peak for Mapreduce, and FIG. 11B is an expandedview of a portion of the graph from FIG. 11A.

FIG. 12A shows graphs of cumulative distribution functions forWebsearch, Webmail, Mapreduce and the mixture of all at the clusterlevel, and FIG. 12B is an expanded view of a portion of the graph fromFIG. 12A.

FIG. 13A shows graphs of cumulative distribution functions for a realdatacenter at the rack, PDU and cluster level, and FIG. 13B is anexpanded view of a portion of the graph from FIG. 13A.

FIG. 14A is a graph showing of the impact on peak power reduction of CPUvoltage scaling at the datacenter level.

FIG. 14B is a graph showing of the impact on energy savings of CPUvoltage scaling at the datacenter level.

14A is a graphs showing of the impact on peak power reduction of CPUvoltage scaling at the datacenter level.

FIG. 15 is a graph of the idle power as a fraction of peak power in fiveserver configurations.

FIG. 16 is a graph of the power and energy savings achievable byreducing idle power consumption to 10% of peak.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Large-scale Internet services and the massively parallel computinginfrastructure that is required to support them can require the designof warehouse-sized computing systems, made up of thousands or tens ofthousands of computing nodes, their associated storage hierarchy andinterconnection infrastructure.

Power is a first-order concern in the design of these massive computingsystems. The cost of powering server systems has been steadily risingwith higher performing systems, while the cost of hardware has remainedrelatively stable. If these trends continue, the cost of the powerconsumed by a server during its lifetime could surpass the cost of theequipment itself.

Another cost factor that has not received significant attention is thecost of building a data center facility having the capacity to deliverthe power required by such a massive computing system. Typical datacenter building costs fall between $10 and $20 per deployed Watt of peakcritical power (power for computing equipment only, excluding coolingand other ancillary loads), and electricity costs in the U.S. areapproximately $0.80/Watt-year (less than that in areas where largedatacenters tend to be deployed). Unlike energy costs that vary withactual usage, the cost of building a data center facility is fixed for agiven peak power delivery capacity. Consequently, the moreunder-utilized a facility, the more expensive it becomes as a fractionthe total cost of ownership. For example, if a facility operates at 85%of its peak capacity on average, the cost of building the facility willstill be higher than all electricity expenses for ten years of operation(e.g., assuming typical Tier-2 datacenter costs of $11/Watt of criticalpower and a 50% energy overhead for cooling and conversion losses).Therefore, there is a strong economic incentive to operate facilities asclose as possible to maximum capacity, so that the non-recurringfacility costs can be best amortized.

Maximizing usage of the available power budget is also important forexisting facilities, since it can allow the computing infrastructure togrow or to enable upgrades without requiring the acquisition of new datacenter capacity, which can take years if it involves new construction.

The power budget available at a given aggregation level is oftenunderutilized in practice, sometimes by large amounts. Some of theimportant contributing factors to underutilization include thefollowing.

Staged deployment—A facility is rarely fully populated upon initialcommissioning, but tends to be sized to accommodate business demandgrowth. Therefore the gap between deployed and used power tends to belarger in new facilities.

Fragmentation—Power usage can be left stranded simply because theaddition of one more unit (a server, rack or PDU) might exceed thatlevel's limit. For example, a 2.5 kW circuit may support only fourservers that draw 520 W peak power, which would guarantee a 17%underutilization of that circuit. If a datacenter is designed such thatthe PDU-level peak capacity exactly matches the sum of the peakcapacities of all of its circuits, such underutilization percolates upthe power delivery chain and become truly wasted at the datacenterlevel.

Conservative equipment ratings—Nameplate ratings in computing equipmentdatasheets typically reflect the worst-case input power of the powersupply instead of the actual peak power draw of the specific equipment.As a result, nameplate values tend to drastically overestimateachievable power draw.

Variable load—Typical server systems consume variable levels of powerdepending on their activity. For example, a typical low-end serversystem consumes less than half its actual peak power when it is idle,even in the absence of any sophisticated power management techniques.Such variability transforms the power provisioning problem into anactivity prediction problem.

Statistical effects—It is increasingly unlikely that large groups ofsystems will be at their peak activity (therefore power) levelssimultaneously as the size of the group increases.

Load variation and statistical effects are the main dynamic sources ofinefficiency in power deployment.

In modern, well designed facilities, both conversion losses and coolingoverheads can be approximately modeled as a fixed tax over the criticalpower used. Less modern facilities might have a relatively flat coolingpower usage that does not react to changes in the heat load. In eithercase, the variations in the critical load will accurately capture thedynamic power effects in the facility, and with the aid of somecalibration can be used to estimate the total power draw.

The incentive to fully utilize the power budget of a datacenter isoffset by the business risk of exceeding its maximum capacity, whichcould result in outages or costly violations of power provisioningservice agreements.

In addition, it is difficult to operates facilities close to maximumcapacity in practice because of uncertainties in equipment power ratingsand because power consumption tends to vary significantly with theactual computing activity.

Effective power forecasting and provisioning strategies are needed todesign a data center facility to deliver the power that will be requiredby the housed computing equipment, and to determine how much computingequipment can be safely and efficiently hosted within a given powerbudget. Determining deployment and power management strategies toachieve near maximum power utilization requires understanding thesimultaneous power usage characteristics of groups of hundreds orthousands of machines, over time. This is complicated by three importantfactors: the rated maximum power (or nameplate value) of computingequipment is usually overly conservative and therefore of limitedusefulness; actual consumed power of servers varies significantly withthe amount of activity, making it hard to predict; differentapplications exercise large-scale systems differently. Consequently onlydirect measurement of the actual power draw at the PDU and datacenterlevel can give accurate power usage characteristics, in particularimplementations.

An exemplary data center facility will be described as an introductionto the power forecasting and provisioning issues.

FIGS. 1A and 1B are side and plan views to illustrate an exemplaryfacility 10 that serves as a data center. The facility 10 includes anenclosed space 12 and can occupy essentially an entire building, or beone or more rooms within a building. The enclosed space 12 issufficiently large for installation of numerous (dozens or hundreds orthousands of) racks of computer equipment, and thus could househundreds, thousands or tens of thousands of computers.

Modules 20 of rack-mounted computers are arranged in the space in rows22 separated by access aisles 24. Each module 20 can include multipleracks 26, and each rack includes multiple trays 28. In general, eachtray 28 can include a circuit board, such as a motherboard, on which avariety of computer-related components are mounted. A typical rack 26 isa 19″ wide and 7′ tall enclosure.

The facility also includes a power grid 30 which, in thisimplementation, includes a plurality of power distribution “lines” 32that run parallel to the rows 22. Each power distribution line 32includes regularly spaced power taps 34, e.g., outlets or receptacles.The power distribution lines 32 could be busbars suspended on or from aceiling of the facility. Alternatively, busbars could be replaced bygroups of outlets independently wired back to the power supply, e.g.,elongated plug strips or receptacles connected to the power supply byelectrical whips. As shown, each module 20 can be connected to anadjacent power tap 34, e.g., by power cabling 38. Thus, each circuitboard can be connected both to the power grid, e.g., by wiring thatfirst runs through the rack itself and the module and which is furtherconnected by the power cabling 38 to a nearby power tap 34.

In operation, the power grid 30 is connected to a power supply, e.g., agenerator or an electric utility, and supplies conventional commercialAC electrical power, e.g., 120 or 208 Volt, 60 Hz (for the UnitedStates). The power distribution lines 32 can be connected to a commonelectrical supply line 36, which in turn can be connected to the powersupply. Optionally, some groups of power distribution lines 32 can beconnected through separate electrical supply lines to the power supply.

Many other configurations are possible for the power grid. For example,the power distribution lines can have a different spacing than the rowsof rack-mounted computers, the power distribution lines can bepositioned over the rows of modules, or the power supply lines can runperpendicular to the rows rather than parallel.

The facility will also include cooling system to removing heat from thedata center, e.g., an air conditioning system to blow cold air throughthe room, or cooling coils that carry a liquid coolant past the racks,and a data grid for connection to the rack-mounted computers to carrydata between the computers and an external network, e.g., the Internet.

The power grid 30 typically is installed during construction of thefacility 10 and before installation of the rack-mounted computers(because later installation is both disruptive to the facility andbecause piece-meal installation may be less cost-efficient). Thus, thesize of the facility 10, the placement of the power distribution lines32, including their spacing and length, and the physical components usedfor the power supply lines, need to be determined before installation ofthe rack-mounted computers. Similarly, capacity and configuration of thecooling system needs to be determined before installation of therack-mounted computers. To determine these factors, the amount anddensity of the computing equipment to be placed in the facility can beforecast.

Before discussing power forecasting and provisioning issues, it isuseful to present a typical datacenter power distribution hierarchy(even though the exact power distribution architecture can varysignificantly from site to site).

FIG. 2 shows the power distribution system 50 of an exemplary Tier-2datacenter facility with a total capacity of 100 KW. The rough capacityof the different components is shown on the left side. A medium voltagefeed 52 from a substation is first transformed by a transformer 54 downto 480 V. It is common to have an uninterruptible power supply (UPS) 56and generator 58 combination to provide back-up power should the mainpower fail. The UPS 56 is responsible for conditioning power andproviding short-term backup, while the generator 58 provides longer-termback-up. An automatic transfer switch (ATS) 60 switches between thegenerator and the mains, and supplies the rest of the hierarchy. Fromhere, power is supplied via two independent routes 62 in order to assurea degree of fault tolerance. Each side has its own UPS that supplies aseries of power distribution units (PDUs) 64. Each PDU is paired with astatic transfer switch (STS) 66 to route power from both sides andassure an uninterrupted supply should one side fail. The PDUs 64 arerated on the order of 75-200 kW each. They further transform the voltage(to 110 or 208 V in the US) and provide additional conditioning andmonitoring, and include distribution panels 65 from which individualcircuits 68 emerge. Circuits 68, which can include the power cabling 38,power a rack or fraction of a rack worth of computing equipment. Thegroup of circuits (and unillustrated busbars) provides the power grid30. Thus, there can be multiple circuits per module and multiplecircuits per row. Depending on the types of servers, each rack 26 cancontain between 10 and 80 computing nodes, and is fed by a small numberof circuits. Between 20 and 60 racks are aggregated into a PDU 64.

Power deployment restrictions generally occur at three levels: rack,PDU, and facility. (However, as shown in FIG. 2, four levels may beemployed, with 2.5 KW at the rack, 50 KW at the panel, 200 KW at thePDU, and 1000 KW at the switchboard.) Enforcement of power limits can bephysical or contractual in nature. Physical enforcement means thatoverloading of electrical circuits will cause circuit breakers to trip,and result in outages. Contractual enforcement is in the form ofeconomic penalties for exceeding the negotiated load (power and/orenergy).

Physical limits are generally used at the lower levels of the powerdistribution system, while contractual limits may show up at the higherlevels. At the rack level, breakers protect individual power supplycircuits 68, and this limits the power that can be drawn out of thatcircuit (in fact the National Electrical Code Article 645.5(A) limitsdesign load to 80% of the maximum ampacity of the branch circuit.).Enforcement at the circuit level is straightforward, because circuitsare typically not shared between users.

At higher levels of the power distribution system, larger power unitsare more likely to be shared between multiple different users. The datacenter operator must provide the maximum rated load for each branchcircuit up to the contractual limits and assure that the higher levelsof the power distribution system can sustain that load. Violating one ofthese contracts can have steep penalties because the user may be liablefor the outage of another user sharing the power distributioninfrastructure. Since the operator typically does not know about thecharacteristics of the load and the user does not know the details ofthe power distribution infrastructure, both tend to be very conservativein assuring that the load stays far below the actual circuit breakerlimits. If the operator and the user are the same entity, the marginbetween expected load and actual power capacity can be reduced, becauseload and infrastructure can be matched to one another.

Turning now to power planning, FIG. 3 is a flowchart illustrating amethod of power planning to permit facility construction and deploymentof computing equipment. The initial two power planning steps are todetermine a design power density for the datacenter (step 102) and todetermine an oversubscription ratio (step 104).

The design power density is a metric of the peak power draw per spatialunit by the rack-mounted computers in the facility. The design powerdensity sets the power-carrying capacity requirement of the power grid.In particular, the design power density can be given in units of powerper length, e.g., kilowatts per foot (kw/ft). This is a useful metricbecause it sets the power-carrying the density of power taps along apower delivery line and the power capacity of the power taps. Inparticular, the number of taps per unit length multiplied by theamperage of those taps cannot exceed the design power density. Thedesign power density also sets the power capacity requirements of thepower supply cabling, e.g., the circuits. The maximum power capacityrequirements of the power distribution lines, e.g., the busbars, can besomewhat lower (on a per circuit basis, not on an absolute basis) thanthe circuits, since averaging discussed below will lower the peak powerdraw. The capacity of the physical components, e.g., the busbars, plugs,switching gear, etc., that compose a line are relatively easy todetermine (e.g., are often given by manufacturer's specifications).Since the power cabling and power distribution lines do not need to beoverdesigned, wasted infrastructure cost is avoided.

The inputs used to determine the design power density include aplatforms roadmap, and facility component restraints. The platformsroadmap provides a projection of the peak power draw of a typical servercomputer at a certain point, e.g., three years or five years, into thefuture. This projection can be calculated by accumulating a data sampleof the peak power draws for server computers over a certain agedistribution, determining the rate of power draw increase, and assumingthat this rate of increase will remain constant. The projection can alsobe taken from information from computer manufacturers themselvesregarding their plans for power draw of future systems.

The platforms roadmap also provides a form factor for the rack-mountedcomputers and racks themselves. That is, the platforms roadmap providesthe number of computers per rack, and the number of racks per unitlength along the row. The form factor can be given by assuming that thecurrent form factor remains unchanged, e.g., that racks are 19″ wide andhold ten to eighty computers.

For example, assuming that a row is to include two racks, each rack is19″ wide and is to hold twenty-five computers, and each future computeris projected to consume 400 watts of power at peak operation, then thedesign power density can be calculated as (400 watts/computer*25computers/rack*2 racks/19″ *12″/foot)≈12.6 kw/ft.

The facility component restraints dictate an upper bound of the powerdensity. In particular, above a certain power capacity, the physicalcomponents (e.g., busbars, plugs) of the power grid become prohibitivelyexpensive. Thus, the calculated design power density can be check forreasonableness, and if necessary reduced to meet practical costlimitations.

The oversubscription ratio is a ratio of design power density to anaverage power density for presently available computers. As describedabove, the design power density is based on the projected peak powerdraw of a future computer. However, the average power draw is usuallylower, and often significantly lower than the peak power draw. There areseveral factors that contribute to this effect. First, as noted above,the power draw of computers is expected to increase, and thus the powerdraw for future computers is set higher than the power draw of presentlyavailable computers. Second, the peak power draw is greater than theaverage power draw. In particular, the individual components (e.g.,circuits) that carry power need to be able to handle the peak power drawof a computer. However, in the aggregate of thousands of computers,statistically there is a much smaller deviation from the average powerdraw.

Another way to consider the oversubscription ratio is as ratio of peakpower draw over the life of a facility at the computer level to the peakpower draw (per presently available computers) at the facility level.Computer utilization may be substituted as a measure of power draw.Thus, the oversubscription ratio can be based on the ratio of peakutilization at the computer level to peak utilization at the facilitylevel.

Inputs for determining the oversubscription ratio include measuredplatform data, measured application data and “forklift” data. Forkliftdata is information regarding a target number of platforms, and theirtype, to move into a facility. Application data includes informationregarding the number of type of applications that will be running on theplatforms. Platform data includes information from which power usage canbe determined for a particular type of platform and application, and caninclude experimentally determined power usage and utilization data forsome platforms.

Initially, power usage data can be collected experimentally. Each datapoint can include an amount of power drawn, the time of the measurement,and identity of the component (e.g., the particular computer, rack orPDU) that is the subject of the measurement. In particular, a cluster ofsimilar platforms can be run using the same application(s). During thistime, the power usage can be measured at the computer level, the racklevel, the PDU level, the cluster level and/or the facility level Themeasurements are performed for sufficient time to obtain a staticallysignificantly sample of power, e.g., for 24 hours. A cluster can beconsidered to be a large number of computers, e.g., five to ten thousandcomputer, that are logically connected, e.g., tasked to perform the sameapplication, e.g., serving search requests. There can be more than onecluster in a facility. In addition, as discussed below, rather thanmeasure power usage directly, utilization can be measured, and powerusage calculated from utilization.

A power distribution function (percentage time spent at or below a givenfraction of peak power) can be determined from the experimentallygathered power usage data at a variety of levels of the powerdistribution system (e.g., at the computer, rack, PDU, cluster orfacility) for a variety of platforms (e.g., each platform is aparticular model of computer with a particular processing speed,available memory, etc.) for a variety of applications (e.g., searchrequests, email, map requests). Average and peak power draw can becalculated from the power distribution function.

In addition, much simpler baseline power usage data can be collected forparticular computers that are proposed to be installed in the facility.This baseline power usage data can be a power distribution function forthe particular computer as determined experimentally by running astandard application, a measured average or peak power usage asdetermined experimentally by running a standard application, or simply anameplate power for collected from manufacturer specifications. Wherepower usage is determined experimentally, the application can be a highdemand application.

The power usage data can be entered into power planning software. Thepower planning software can include a power usage database that storesthe data, and a power calculation algorithm. The algorithm canextrapolate the expected power usage from a baseline for a proposedcomputer based on the relationships between known power distributionfunctions. For example, once a power distribution function is measuredfor a proposed new server running a standard search service, then thepeak power usage for a cluster of such servers running such a searchservice can be extrapolated using the relationship between the measuredpower distribution functions at the computer and cluster level for otherservers running the search service.

In some implementations, the baseline power usage data includessufficient data from which a power as a function of utilization, e.g.,CPU utilization, can be derived for a particular platform andapplication. For example, the database can include experimentallydetermined power draw for a given platform and a given application atseveral different utilization levels. From this data, a linearregression can be used to fit the power draw as a linear function of CPUutilization.

The baseline data can also include sufficient data from which power as afunction of utilization can be determined for combinations of platformsand application that have not been experimentally measured. The baselinedata can include either a specific model that relates components to thepower-utilization function, or data from which a relationship ofcomponents to the power-utilization can be derived. For example, thebaseline data can include a components database indicating theparticular components, e.g., the class and speed of the CPU, the amountof memory, and the number of disk drives, for a particular type ofplatform. The baseline power data can also include experimentallydetermined power draw for platforms with different components. Thebaseline power data can also include experimentally determined powerdraw for platforms running different applications. The power-utilizationfunction for a platform with a particular combination of components andapplication (that has not been experimentally measured) can becalculated, e.g., by interpolation, from the data of other platformswith different combinations of components and applications (which havebeen experimentally measured).

In some implementations, the power-utilization function gives power as alinear function of utilization, with the offset and slope of the linebeing dependent on the components of the platform and the application.The offset and slope can thus be values that are calculated fromavailable power usage data.

The power usage of a computer can be determined by measuring theutilization and calculating the power from the utilization and thepower-utilization function. The power usage of a rack, PDU or clustercan be determined measuring the utilization of all of the computersfollowed by calculating the power from their power-utilizationfunctions, or measuring the utilization of some of the computers, e.g.,a statistical sampling, calculating the power from the power-utilizationfunctions of the sample, and extrapolation from the power draw of thesample to the power draw of the rack, PDU or cluster.

To determine the expected maximum power usage for a computer, thehistorical utilization data can be analyzed and an expected maximumutilization can be set as the utilization below which the computerspends the vast majority of time as given by a distribution percentile,e.g., at least 95%, e.g., at least 98%, e.g., at least 99%. The expectedmaximum power usage can then be calculated from the expected maximumutilization and the power-utilization function.

Using the power planning software, an expected power usage can becalculated for an exemplary platform and a desired application. Theexemplary platform is selected to be somewhat less demanding thanexpected future platforms, although possibly somewhat more demandingthan a “mid-performance” platform available at the time of design. Giventhe exemplary platform and the desired application, the expected powerusage can be determined at the facility level, and an oversubscriptionratio calculated.

In general, either step 102 or step 104 can be performed first. However,once determined, a design power density can be used for multiplefacilities, whereas the oversubscription ratio depends on theapplications to be performed by the computing system to be installed ina particular facility, and thus can vary from facility. Thus, the designpower density tends to be determined first and remain constant forrepetition of the oversubscription step for different facilities.

Once the design power density and oversubscription ratio are determined,spatial planning can be performed (step 106). Spatial planning includesdetermination of size of the facility and layout of the powerdistribution lines and taps. In particular, given the available powerfor the facility (e.g., 1 to 2 megawatts), the total length of therow(s) of racks can be calculated by dividing available power by thedesign power density and multiplying by the oversubscription ratio.

For example, assuming that the available power is 1.5 megawatts, thedesign power density is 12.6 kw/ft, and the oversubscription ratio is 2,the total row length will be given by (1.5 megawatts/12.6 kw/ft*2)≈238feet. This could be divided into a single row of 238 feet, two rows of119 feet, three rows of 80 feet, and so on. In contrast, if theoversubscription ratio is 1.0, then the total row length would be 119feet, which could be provided by a single row of 119 feet, two rows of60 feet, etc.

In general, in order to operate the facilities close to maximum powercapacity, additional computers are added beyond those that would use theentire power capacity if operating at peak power usage.

The data center facility can now be constructed in accordance with thespatial plan and the design power density (step 108). Thus, the facilitycan be constructed with an interior volume sufficient to house the totalrow length calculated above. In addition, the facility can be installedwith power distribution lines configured to handle the design powerdensity, and extending through the facility space with sufficient lengthand spacing to cover the total row length.

In addition, expected heat dissipation can be calculated from the designpower density (in general, nearly all of the power consumed by therack-mounted computers is converted to heat). A cooling system can alsobe installed in the data center facility with sufficient coolingcapacity to expected heat dissipation from the rack-mounted computers.

Deployment of actual rack-mounted computers can be planned (step 110).Deployment planning ensures that a given combination of platforms andapplications do not exceed the available power and design power densityfor the facility. Thus, given an expected usage of the data center(generally as driven by customer demand), e.g., for search requests,email, map requests and the like, and given an intended platforminstallation, an expected power usage particularized for application andplatform can be generated. By summing the expected power usage for eachset of platforms and applications, a total expected power usage can becalculated. The expected power usage can be an expected average powerusage or an expected maximum power usage.

FIG. 4 shows software to perform this calculation can be implemented,e.g., as a spreadsheet, with each record (e.g., row) having field fornumber of units, particular platform and particular application. Theexpected power usage per unit can be determined in another field from alookup table in the spreadsheet that uses the selected platform andapplication, and this value can be multiplied by the number of units toprovide a subtotal. The lookup table can calculate the expected powerusage from an expected utilization (which can be set for all recordsfrom a user-selected distribution percentile) and the power-utilizationfunction for the combination of platform and application. Finally, thesubtotals from each row can be totaled to determine the total powerusage.

The deployment planning step can be performed in conjunction with othersteps.

Actual rack-mounted computers can now be deployed into the facility inaccordance with the plan (step 112).

Once some rack-mounted computers are installed and operating, furtherpower consumption data can be collected to refine the power plannerdatabase. In addition, the effects of planned changes, e.g., platformadditions or upgrades, can be forecast, effectively repeating thedeployment planning step.

In general, this design and deployment scheme balances the short-termand long-term usage of the facility. Although an initial serverinstallation may not use all of the available power, the excess capacitypermits equipment upgrades or installation of additional platforms for areasonable period of time without sacrificing platform density. On theother hand, once available power has been reached, further equipmentupgrades can still be performed, e.g., by decreasing the platformdensity (either by fewer computer per rack or by greater spacing betweenracks) or by using lower power applications, to compensate for theincreased power consumption of the newer equipment.

This design and deployment scheme also permits full utilization of thetotal power available to the facility, while designing powerdistribution components within the power distribution network withsufficient capacity to handle peak power consumption. As shown in FIG.5, a power distribution network might have three PDUs. Although thefacility might have 1 megawatt available, each PDU may need 500 KWcapacity to handle transient power peaks. If each PDU were run at fullcapacity, this would exceed the capacity of the facility. By using thepower profiling software, the average total power used by therack-mounted computers can be calculated to confirm that a potentialdeployment does not exceed the maximum capacity of the facility.Moreover, the percentage usage of the available facility power need notbe uniform, but can vary from branch to branch in the distributionhierarchy.

It should also be noted that this scheme permits installation andoperation of more rack-mounted computers for a given power budget thanwould be expected given the peak power draw for a particular computer.In particular, installation can be performed based on the peak powerrequired by a cluster or higher level, rather than the peak powerrequired for a particular rack-mounted computer. In short, at thecluster level, due to the much larger number of computers, power draw ismore uniform and peak power draw is closer to the average power draw,than at the platform or rack level.

For example, for a particular application, a particular platform mighthave an average power draw of 350 watts and peak draw of 400 watts. Ifinstallation was performed based on the peak draw of the platform, thenthe assumption would be that a 1 megawatt capacity facility could holdno more than 2500 such platforms. However, a cluster of platformsrunning the application might have an average power draw of 350watts/computer and a peak draw of 375 watts/computer. Thus, a facilitywith 1 megawatt capacity could actually hold 2666 such platforms safely.

In addition, the effects of deployment can be evaluated “end to end” inthe data center. The power usage can be calculated at the rack, STS, PDUand facility level. Thus, deployment can be evaluated for compliancewith available capacity at each of these levels.

Once the computers have been deployed, power usage can be monitored onan ongoing (although not necessarily real-time) basis. For example, itis possible to monitor the power usage at the PDU and STS levels. Finermonitoring granularity permits greater control of load balancing. Forexample, it would be possible to monitor the power usage of each rack oreven each computer, although the latter can have significant equipmentcost. Another implementation would be to monitor a statistical samplingof computers, e.g., one computer per rack.

In addition, monitoring can be performed “end to end” in the datacenter. That is power usage can be monitored at the rack, PDU, STS andfacility level. In particular, the power hierarchy can be stored in thedatabase such that power usage data for subcomponents of a particularcomponent (e.g., racks within a particular PDU), can be summed toprovide the total usage of the particular component.

Once the rack-mounted computers are deployed and operating, it is alsopossible to perform dynamic power management.

One form of dynamic power management is job allocation, i.e., thedecision regarding which tasks are to be performed by which computers.Job allocation can use power management software. The power managementsoftware can be similar to the power planning software described above,e.g., able to calculate a peak power usage at the facility, PDU and racklevel for given platforms running given applications. For example, thesoftware could maintain a running total of the load on each component ofthe system based on the assigned job type (e.g., application) andplatform (and this would be an indication of the power load, not simplya count of the number of jobs). Jobs can then be preferentially assignedto computers in racks or PDUs with a lower power load so as to avoidoverloading any particular component. As another example, if powermonitoring indicates that a particular branch of the hierarchy (e.g., arack) is operating dangerously near maximum capacity, then jobs can beredirected to other portions of the data center. The effect of aproposed allocation of a job to a computer or rack can be evaluated toensure that capacity of components in the corresponding branches of thepower hierarchy are not exceeded.

Another form of dynamic power management is job scheduling, which can beperformed at the machine level. For dynamic job scheduling, as the powerusage increases, some jobs can be delayed. For example, if measuredpower usage for a section of the power distribution hierarchy, e.g., arack or PDU, is approaching its capacity, then a signal can be sent tothe computers within that section. In response, those computers candelay some jobs, e.g., low priority jobs.

Yet another form of dynamic power management is execution rate control,which can be performed at the machine level. For dynamic execution ratecontrol, as the power usage increases, the execution frequency of thecomputers can be reduced. For example, if measured power usage for asection of the power distribution hierarchy, e.g., a rack or PDU, isapproaching its capacity, then a signal can be sent to the computerswithin that section. In response, the those computers can reduce theirexecution rate, thus reducing their power consumption.

Experimental Results

The power usage characteristics of three large-scale workloads as wellas a workload mix from an actual datacenter, each using up to severalthousand servers, over a period of about six months are presented.Critical power and how power usage varies over time and over differentaggregation levels (from individual racks to an entire cluster) isexamined below. A light-weight yet accurate power estimation methodologyis used that is based on real time activity information and the baselineserver hardware configuration. The model permits estimation of thepotential power and energy savings of power management techniques, suchas power capping and CPU voltage/frequency scaling.

The aggregate power usage characteristics of large collections ofservers (up to 15 thousand) for different classes of applications over aperiod of approximately six months are described. The observations allowopportunities for maximizing the use of the deployed power capacity ofdatacenters to be evaluated, and the risks of over-subscribing to beassessed (in this context, over-subscribing refers to the danger ofpower usage exceeding the power budget at a particular level of powerdistribution).

Even in well-tuned applications, there is a noticeable gap (7-16%)between achieved and theoretical aggregate peak power usage at thecluster level (thousands of servers). The gap grows to almost 40% inwhole datacenters. This headroom can be used to deploy additionalcomputer equipment within the same power budget with minimal risk ofexceeding it. A modeling framework is used to estimate the potential ofpower management schemes to reduce peak power and energy usage. Theopportunities for power and energy savings are significant, but greaterat the cluster-level (thousands of servers) than at the rack-level(tens). Finally, systems can be power efficient across the activityrange, and not only at peak performance levels.

To inventors' knowledge, this is the first power usage study of verylarge scale systems running real live workloads, and the first reporteduse of power modeling for power provisioning. Some findings andcontributions include the following.

First, the gap between the maximum power actually used by large groupsof machines and their aggregate theoretical peak usage can be as largeas 40% in datacenters, suggesting a significant opportunity to hostadditional machines under the same power budget. This gap is smaller butstill significant when well-tuned large workloads are considered.

Second, power capping using dynamic power management can enableadditional machines to be hosted, but is more useful as a safetymechanism to prevent overload situations.

Third, there are time intervals when large groups of machines areoperating near peak power levels, suggesting that power gaps and powermanagement techniques might be more easily exploited at thedatacenter-level than at the rack-level.

Fourth, CPU voltage/frequency scaling, a technique targeted at energymanagement, has the potential to be moderately effective at reducingpeak power consumption once large groups of machines are considered.

One of the difficulties of studying power provisioning strategies is thelack of power usage data from large-scale deployments. In particular,most facilities lack on-line power monitoring and data collectionsystems that are needed for such studies. This problem can becircumvented by deploying an indirect power estimation framework that isflexible, low-overhead and yet accurate in predicting power usage atmoderate time intervals. This section begins by describing a framework,and presenting some validation data supporting its accuracy.

Initially, the power usage profile of a typical server and how nameplateratings relate to the actual power draw of machines will be examined.

A server is typically tagged with a nameplate rating that is meant toindicate the maximum power draw of that machine. The main purpose ofthis label is to inform the user of the power infrastructure required tosafely supply power to the machine. As such, it is a conservative numberthat is guaranteed not to be reached. It is typically estimated by theequipment manufacturer simply by adding up the worst case power draw ofall components in a fully configured system.

Table 1 below shows the power draw breakdown for a server built out of amotherboard with 2 ×86 CPUs, an IDE disk drive, 4 slots of DDR1 DRAM,and 2 PCI expansion slots. Using the maximum power draw taken from thecomponent datasheets, a total DC draw of 213 W is calculated.

TABLE 1 Component Peak Power Count Total Component Peak Power CountTotal CPU [16] 40 W 2 80 W Memory [18]  9 W 4 36 W Disk [24] 12 W 1 12 WPCI Slots 25 W 2 50 W [22] Motherboard 25 W 1 25 W Fan 10 W 1 10 WSystem Total 213 W 

Assuming a power supply efficiency of 85%, a total nameplate power of251 W is calculated.

However, when the power consumption of this server is actually measuredusing the most power intensive benchmarks, only a maximum of 145 W isreached, which is less than 60% of the nameplate value. We refer to thismeasured rating as the actual peak power. As this example illustrates,actual peak power is a much more accurate estimate of a system's peakconsumption, therefore we choose to use it instead of nameplate ratingsin our subsequent analysis.

The breakdown shown in Table 1 does nevertheless reflect the powerconsumption breakdown in a typical server. CPUs and memory dominatetotal power, with disk power becoming significant only in systems withseveral disk drives. Miscellaneous items such as fans and themotherboard components round out the picture.

A power model uses CPU utilization as the main signal of machine-levelactivity. For each family of machines with similar hardwareconfiguration, a suite of benchmarks was run that includedrepresentative workloads as well as a few microbenchmarks, undervariable loads. The total system power was measured against CPUutilization, and a curve that approximates the aggregate behavior wasfound.

FIG. 6 shows experimental measurements alongside a linear model and anempirical non-linear model that more closely fits the observations. Thehorizontal axis shows the CPU utilization (u) reported by the OS as anaverage across all CPUs. A calibration parameter r that minimizes thesquared error is chosen (a value of 1.4 in this case). For each class ofmachines deployed, one set of calibration experiments is needed toproduce the corresponding model.

The error bars in FIG. 6 give a visual indication that such models canbe reasonably accurate in estimating total power usage of individualmachines. Of greater interest to this study, however, is the accuracy ofthis methodology in estimating the dynamic power usage of groups ofmachines. FIG. 7 shows how the model compares to the actual measuredpower drawn at the PDU level (a few hundred servers) in a productionfacility. Note that except for a fixed offset, the model tracks thedynamic power usage behavior extremely well. In fact, once the offset isremoved, the error stays below 1% across the usage spectrum and over alarge number of PDU-level validation experiments.

The fixed offset is due to other loads connected to the PDUs that arenot captured by our model, most notably network switching equipment.Networking switches operate on a very narrow dynamic range (measurementsshow that Ethernet switch power consumption can vary by less than 2%across the activity spectrum), therefore a simple inventory of suchequipment, or a facility-level calibration step is sufficient for powerestimation.

This single activity level signal (CPU utilization) produces veryaccurate results, especially when larger numbers of machines areconsidered. The observation can be explained by noting that CPU andmemory are in fact the main contributors to the dynamic power, and othercomponents either have very small dynamic range (the componentmeasurements showed that the dynamic power range is less than 30% fordisks, and negligible for motherboards) or their activity levelscorrelate well with CPU activity. Therefore, it was unnecessary to usemore complex models and additional activity signals (such as hardwareperformance counters).

Similar modeling methodology can be useful in informing powerprovisioning plans.

The Data Collection Infrastructure. In order to gather machineutilization information from thousands of servers, a distributedcollection infrastructure was used as shown in FIG. 8. At the bottomlayer, collector jobs gather periodic data on CPU utilization from allservers in the collection. The collectors write the raw data into acentral data repository. In the analysis layer, different jobs combineCPU activity with the appropriate models for each machine class, derivethe corresponding power estimates and store them in a data repository intime series format. Analysis programs are typically built using Google'sMapreduce framework.

Power Usage Characterization. Next, a baseline characterization of thepower usage of three large scale workloads and an actual wholedatacenter, based on six months of power monitoring observations, ispresented.

Three workloads were selected that are representative of different typesof large-scale services. The characteristics of these workloads that arerelevant to this study are briefly described below.

Websearch: This represents a service with high request throughput and avery large data processing requirements for each request. Machinesdeployed in Google's Web search services were measured. Overall activitylevel is generally strongly correlated with time of day, given theonline nature of the system.

Webmail: This represents a more disk I/O intensive Internet service.Servers running GMail, a web-based email product with sophisticatedsearching functionality, were measured. Machines in this service tend tobe configured with a larger number of disk drives, and each requestinvolves a relatively small number of servers. Like Websearch, activitylevel is correlated with time of day.

Mapreduce: This is a cluster that is mostly dedicated to running largeoffline batch jobs, of the kind that are amenable to the Mapreduce styleof computation. The cluster is shared by several users, and jobstypically involve processing terabytes of data, using hundreds orthousands of machines. Since this is not an online service, usagepatterns are more varied and less correlated with time of day.

A sample of approximately five thousand servers running each of theworkloads above were selected. In each case, the sets of serversselected are running well-tuned workloads and typically at high activitylevels. Therefore these servers are representative of the more efficientdatacenter-level workloads, in terms of usage of the available powerbudget.

The main results are shown as cumulative distribution functions (CDFs)of the time that a group of machines spends at or below a given fractionof their aggregate peak power (see for example FIGS. 9A and 9B). Foreach machine, the average power over 10 minute intervals was derivedusing the power model described earlier. The aggregate power for eachgroup of 40 machines during an interval makes up a rack power value,which is normalized to their actual peak (i.e., the sum of the maximumachievable peak power consumption of all machines in the group). Thecumulative distribution of these rack power values is the curve labeled“Rack” in the graph. The “PDU” curve represents a similar aggregation,but now grouping sets of 20 racks (or about 800 machines). Finally, the“Cluster” curve shows the CDF for all machines (approximately 5000machines).

Turning to the power CDF for Websearch, shown in FIGS. 9A and 9B, theRack CDF starts at around 0.45 of normalized power, indicating that atno time does any one rack consume less than 45% of its actual peak. Thisis likely close to the idle power of the machines in the rack. The curverises steeply, with the largest fraction of the CDF (i.e. the most time)spent in the 60-80% range of actual peak power. The curve intercepts thetop of the graph at 98% of the peak power, indicating that there aresome time intervals where all 40 machines in a given rack are operatingvery close to their actual peak power. The graph of FIG. 9 zooms in onthe upper part of the CDF, to make the intercepts with the top of thegraph clearer. The PDU and Cluster curves tend to have progressivelyhigher minimum power and lower maximum power. The larger the group ofmachines is, the less likely it is that all of them are simultaneouslyoperating near the extreme minimum or maximum of power draw. ForWebsearch, some racks are reaching 98% of actual peak power for sometime interval, whereas the entire cluster never goes over 93%. It isstriking to see that groups of many hundreds of machines (PDU-level) canspend nearly 10% of the time within 10% of their aggregate peak power.

The corresponding CDFs for Webmail are shown in FIGS. 10A and 10B. Theshape of these is similar to that of Websearch, with two notabledifferences: the dynamic range of the power draw is much narrower, andthe maximum power draw is lower. Webmail machines tend to have moredisks per machine, and disk power draw does not vary significantly withchanges in activity levels. Hence a larger fraction of the power draw ofthese machines is fixed and the dynamic range is reduced. The max powerdraw is also lower. Interestingly, there is a maximum of about 92% ofpeak actual power at the rack level, and 86% at the cluster level; aneven higher gap than Websearch.

The curves for Mapreduce, as shown in FIGS. 11A and 11B, show a largerdifference between the Rack, PDU, and Cluster graphs than both Websearchand Webmail. This indicates that the power draw across different racksis much less uniform; likely a result of its less time-dependentactivity characteristics. This behavior leads to a much more noticeableaveraging effect at the cluster level. While the racks top out at veryclose to 100% of peak actual power, the cluster never goes above about90%.

These results are significant for machine deployment planning. If themaximum power draw of individual machines to provision the datacenter isused, some capacity will be stranded. For Websearch, about 7.5% moremachines could be safely deployed within the same power budget. Thecorresponding numbers for Webmail and Mapreduce are even higher, at 16%and 11%.

The impact of diversity—FIGS. 12A and 12B present the power CDF when allthe machines running the three workloads are deployed in a hypotheticalcombined cluster. This might be representative of a datacenter-levelbehavior where multiple high-activity services are hosted. Note that thedynamic range of the mix is narrower than that of any individualworkload, and that the highest power value achieved (85% of actual peak)is also lower than even that of the lowest individual workload (Webmailat 86%). This is caused by the fact that power consumption peaks areless correlated across workloads than within them. It is an importantargument for mixing diverse workloads at a datacenter, in order tosmooth out the peaks that individual workloads might present. Using thehighest power of the mix to drive deployment would allow 17% moremachines to be deployed to this datacenter.

An actual datacenter—The examples above concern large, well tunedworkloads in a fully deployed environment. In a real datacenter therewill be additional workloads that are less well-tuned, still indevelopment, or simply not highly loaded. For example, machines can beassigned to a service that is not yet fully deployed, or might be invarious stages of being repaired or upgraded, etc. FIGS. 13A and 13Bshow the power CDF for one such datacenter. This power CDF exhibits thesame trends as seen in the workload mix, only much more pronounced.Overall dynamic range is very narrow (52-72%) and the highest powerconsumption is only 72% of actual peak power. Using this number to guidedeployment would present the opportunity to host a sizable 39% moremachines at this datacenter.

One of the features that stands out in the power CDF curves presented inthe previous section is that the CDF curve intercepts the 100% line at arelatively flat slope, indicating that there are few time intervals inwhich close to the highest power is drawn by the machines. If those fewintervals could be removed, the number of machines hosted within a givenpower budget could be further increased. Power capping techniquesaccomplish that by setting a value below the actual peak power andpreventing that number from being exceeded through some type of controlloop. There are numerous ways to implement this, but such techniquesgenerally include of a power monitoring system (possibly such as thatdescribed above or one based on direct power sensing) and a powerthrottling mechanism. Power throttling generally works best when thereis a set of jobs with loose service level guarantees or low prioritythat can be forced to reduce consumption when the datacenter isapproaching the power cap value. Power consumption can be reduced simplyby descheduling tasks or by using any available component-level powermanagement knobs or control functions, such as CPU voltage/frequencyscaling.

Note that the power sensing/throttling mechanisms needed for powercapping are likely needed anyway even if power is not capped, in orderto take advantage of the power usage gaps shown in the CDF graphs. Inthose cases it is desirable to insure against poorly-characterizedworkloads or unexpected load spikes.

Table 2, below, presents the gains that could be achieved with such ascheme. For each workload, the potential for increased machinedeployment is shown, given an allowance of 1 or 2% of time spent inpower-capping mode. The no power capping numbers are also included forcomparison. Websearch and Webmail (by themselves) are excluded frompower capping, because given their online nature they might not havemuch opportunity for power reduction at peak load.

TABLE 2 Impact of Power Capping Percentage of Time in Power- Increase inN. of Median Avg Capping Machine Intervals Interval Interval WorkloadMode Deployment per Month (min) (min) Websearch 0% 7.0% — — — Webmail 0%15.6% — — — Mapreduce 0% 11.0% — — — 1% 21.5% 21.0 10.0 20.5 2% 23.8%38.8 20.0 22.2 Mix 0% 17.1% — — — 1% 21.7% 12.2 20.0 35.3 2% 23.5% 23.120.0 37.3 Real 0% 39.1% — — — Datacenter 1% 44.7% 9.0 20.0 47.9 2% 46.0%12.5 40.0 69.3

Overall, the additional gains in machine deployment are noticeable butrelatively modest. Generally, 1% captures most of the benefits with onlylittle additional gains for 2% of capping time. The best case isMapreduce, which shows an increase from 11% A in potential increasedmachine deployment without power capping, to 24% with capping 2% of thetime. Notably, mixing the workloads diminishes the relative gains,because the different workloads are already decreasing the likelihood ofa simultaneous power spike in all machines.

Table 2 also shows the number and length of power-capping intervals thatwould be incurred for each workload. This information gives some insightinto how often the power capping system would be triggered, which inturn is useful for deciding on what kind of mechanism to use. Fewer,longer intervals are probably more desirable, because there is alwayssome loss upon entering and leaving the power capping interval.

Perhaps the biggest advantage of dynamic power capping is that it canrelax the requirement to accurately characterize workloads prior todeployment, and provide a safety valve for cases where workload behaviorchanges unexpectedly.

Another interesting observation that can be derived from our data is thedifference between the average and observed peak power draw of aworkload or mix of workloads. While peak power draw is the mostimportant quantity for guiding the deployment of machines to adatacenter, average power is what determines the power bill.Load-dependent power variations are one of the factors leading toinefficient use of the power budget, as discussed above, and can bequantified.

Table 3, below, shows the ratio of average power to observed peak power(over the half-year interval) for the different workloads and mixes ofworkloads. The ratios reflect the different dynamic ranges for thedifferent workloads: Websearch has the highest dynamic range and lowestaverage to peak ratio at 73%. Mapreduce is somewhat higher, and Webmailhas the highest ratio at close to 90%. The two mixed workloads also showhigher ratios, with 84% for the mix of the three tuned workloads, and83% for the real datacenter.

TABLE 3 Average and observed peak power (normalized to actual peak) atthe cluster level Average Observed Workload Power Peak PowerAverage/Observed Websearch 68.0% 93.5% 72.7% Webmail 77.8% 86.5% 89.9%Mapreduce 69.6% 90.1% 77.2% Mix 72.1% 85.4% 84.4% Real DC 59.5% 71.9%82.8%

The mix of diverse workloads generally reduces the difference betweenaverage and peak power, another argument in favor of this type ofdeployment. Note that even for this best case, on the order of 15% ofthe power budget remains stranded simply because of the differencebetween average and peak power, which further increases the relativeweight of power provisioning costs over the cost of energy.

In the previous section, the power modeling infrastructure was used toanalyze actual power consumption of various workloads. In the nextsection, the same activity data from our machines over the six monthtime period is used to simulate the potential for power and energysaving of two schemes: CPU voltage and frequency scaling, and improvingnon-peak power efficiency.

CPU voltage and frequency scaling (DVS for short) is a useful techniquefor managing energy consumption that has recently been made available toserver-class processors. Here the power model is used to predict howmuch energy savings and peak power reductions could have been achievedhad power management techniques based on DVS been used in the workloadsanalyzed in the previous section.

For simplicity and for the purpose of exploring the limits of thebenefit, an oracle-style policy is used. For each machine and each datacollection interval, if the CPU utilization is below a certainthreshold, DVS activation is simulated by halving (there are variousCPUs in the market today that are capable of such power reductionsthrough DVS) the CPU component of the total power, while leaving thepower consumption of the remaining components unchanged.

How system performance might be affected by DVS cannot be determinedwithout detailed application characterization. Therefore three CPUutilization thresholds can be simulated for triggering DVS: 5%, 20%,50%. A 5% threshold is selected as a conservative threshold to examinehow much benefit can be achieved with almost no performance impact. A50% threshold is selected as a very aggressive threshold for thescenario where performance can be degraded significantly or theapplication has substantial amount of performance slack.

FIGS. 14A and 14B show the calculated impact of CPU DVS at the clusterlevel on the three workloads and on the real datacenter. DVS has a moresignificant potential impact on energy than peak power, with savings ofover 20% when using the more aggressive threshold in two out of fourcases. This can be explained since in periods of cluster-wide peakactivity it is unlikely that many servers will be below the DVS triggerthreshold. It is still surprising that there are cases where DVS candeliver a moderate but noticeable reduction in maximum observed power.This is particularly the case for the real datacenter, where theworkload mix enables peak power reductions between 11-18%.

Among the three workloads, Websearch has the highest reduction in bothpeak power and energy. Websearch is the most compute intensive workload,therefore the CPU consumes a larger percentage of the total machinepower, allowing DVS to produce larger reductions relative to totalpower. DVS achieves the least energy savings for Webmail, which has thenarrowest dynamic power range and relatively high average energy usage.Webmail is generally deployed on machines with more disks, and thereforethe CPU is a smaller contributor to the total power, resulting in acorrespondingly smaller impact of DVS. Mapreduce shows the leastreduction in peak power, since it also tends to use machines with moredisks while achieving even higher peak power usage than Webmail. Thesetwo factors create the most difficult scenario for DVS.

It is also worth noting that due to our somewhat coarse data collectioninterval (10 min) the DVS upside is somewhat underestimated here. Theswitching time of the current DVS technology can accommodate asub-second interval, so bigger savings might be possible usingfiner-grained triggers.

Power efficiency of computing equipment is almost invariably measuredwhen running the system under maximum load. Generally when “performanceper Watt” is presented as a rating, it is implicitly understood that thesystem was exercised to maximum performance, and upon reaching that thepower consumption was measured. However, as the analysis in the previoussection showed, the reality is that machines operate away from peakactivity a good fraction of the time. Therefore it is important toconserve power across the activity spectrum, and not just at peakactivity.

FIG. 15 shows the power consumption at idle (no activity) as a fractionof peak power from five of the server configurations we deploy. Idlepower is significantly lower than the actual peak power, but generallynever below 50% of peak. Ideally, systems should consume no power whenidle, and for power to increase roughly proportionally with increasedactivity; a behavior similar to the curves in FIG. 6 but where idleP_(idle) is near zero. Arguably, systems with this behavior would beequally power efficient regardless of activity level. To assess thebenefits of such behavioral change, the model was altered so that idlepower for every machine was set to 10% of the actual peak power. Allother model parameters, including actual peak power, remained the sameas before.

The results, shown in FIG. 16, reveal that the gains can be quitesubstantial. The maximum cluster-level peak power was reduced between6-20% for our three workloads, with corresponding energy savings of35-40%. In a real datacenter, however, the observed maximum powerconsumption dropped over 30%, while less than half the energy was used.The fact that such dramatic gains are possible without any changes topeak power consumption strongly suggest that system and componentdesigners should strive to achieve such behavior in real servers.

It is important to note that the machines in this study, especially theones running the three workloads, were rarely fully idle. Therefore,inactive power modes (such as sleep or standby modes) are unlikely toachieve the same level of savings.

Power Provisioning Strategies. From the results in the previoussections, some conclusions can be drawn about strategies for maximizingthe amount of compute equipment that can be deployed at a datacenterwith a given power capacity.

First of all, it is important to understand the actual power draw of themachines to be deployed. Nameplate power figures are so conservative asto be useless for the deployment process. Accurate power measurements ofthe machines can be used in the actual configurations to be deployed andrunning benchmarks that maximize overall power draw.

The characterization of application power draw at different levels ofdeployment granularity allows potential for safely over-subscribingpieces of the power distribution hierarchy to be judged.Over-subscription (in this context, exceeding the power budget) at therack level is not safe. In both Websearch and Mapreduce, individualracks approach very close to peak actual power during some timeintervals. Webmail has a little room for over-subscription at the racklevel, at 92%. At the PDU level, more potential for over-subscriptionexists. At the cluster level, there is a noticeable difference betweenobserved and actual peak power, allowing for the deployment of between7-16% more machines for individual applications. The headroom increaseswhen applications are mixed together, indicating that it is desirable todo so. Mixing also leads to a narrowing of average to peak power, whichis desirable from a utilization of infrastructure standpoint. Finally,we have shown that in a real cluster the deployment of less well tunedapplications and other conditions leading to poorly-utilized machinescan drive the headroom close to 40%. Once again, this is using peakactual power to guide deployment. The more common practice of usingnameplate power further inflates these numbers, leading to headroom for80-130% more machines to be deployed.

A dynamic power management scheme to cap the peak power draw at somepre-determined value has two advantages. First of all, it can act as asafety valve, protecting the power distribution hierarchy againstoverdraw. It thus allows for aggressive deployment of machines, even inthe face of poorly characterized applications or unexpected load spikes.Secondly, it enables additional over-subscription of the availablepower. Capping power for even a small fraction of overall time candeliver noticeable additional gains in machine deployment.

While dynamic voltage/frequency scaling may not produce much reductionof peak power draw at the rack level, there is a noticeable reduction atthe cluster level. Depending on application, peak power reductions of upto 10% are seen for aggressive schemes, growing up to 18% for the realdatacenter workload mix. Even the least aggressive scheme netted an 11%reduction in peak power for the real datacenter mix.

Some of the computing systems being built today look more like awarehouse than a refrigerator. Power provisioning decisions for suchsystems can have a dramatic economic impact as the cost of buildinglarge datacenters could surpass the cost of energy for the lifetime ofthe facility. Since new datacenter construction can take tens of months,intelligent power provisioning also has a large strategic impact as itmay allow an existing facility to accommodate the business growth withina given power budget.

How power usage varies over time, and as the number of machinesincreases from individual racks to clusters of up to five thousandservers has been studied. By using multiple production workloads, howpower usage patterns are affected by workload choice can be quantified.The understanding of power usage dynamics can inform the choice of powermanagement and provisioning policies, as well as quantify the potentialimpact of power and energy reduction opportunities. To inventors'knowledge, this is the first power usage study at the scale ofdatacenter workloads, and the first reported use of model-based powermonitoring techniques for power provisioning in real production systems.

Nameplate ratings are of little use in power provisioning as they tendto grossly overestimate actual maximum usage. Using a more realisticpeak power definition, the gaps between maximum achieved and maximumtheoretical power consumption of groups of machines can be quantified.These gaps would allow hosting between 7% and 16% more computingequipment for individual (well-tuned) applications, and as much as 39%in a real datacenter running a mix of applications, through carefulover-subscription of the datacenter power budget. Power cappingmechanisms can enable those opportunities to be capitalized upon byacting as a safety net against the risks of over-subscription, and arethemselves able to provide additional albeit modest power savings.However, over-subscribing power at the rack level is quite risky, giventhat large Internet services are capable of driving hundreds of serversto high-activity levels simultaneously. The more easily exploitableover-subscription opportunities lie at the facility level (thousands ofservers).

CPU dynamic voltage/frequency scaling might yield moderate energysavings (up to 23%). Although it has a more limited peak power savingspotential, it is still surprising that a technique usually dismissed forpeak power management can have a noticeable impact at the datacenterlevel.

Finally, component and system designers should consider power efficiencynot simply at peak performance levels but across the activity range, aseven machines used in well tuned large scale workloads will spend asignificant fraction of their operational lives below peak activitylevels. Peak power consumption at the datacenter level could be reducedby up to 30% and energy usage could be halved if systems were designedso that lower activity levels meant correspondingly lower power usageprofiles.

Various steps of the design processes discussed above, such as an stepsinvolving calculation, particularly determining the design power densityand the oversubscription ratio, can be performed by computer.

Embodiments of the invention and all of the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructural means disclosed in this specification and structuralequivalents thereof, or in combinations of them. Embodiments of theinvention can be implemented as one or more computer program products,i.e., one or more computer programs tangibly embodied in an informationcarrier, e.g., in a machine readable storage device or in a propagatedsignal, for execution by, or to control the operation of, dataprocessing apparatus, e.g., a programmable processor, a computer, ormultiple processors or computers.

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Accordingly, other embodiments are within the scope of the followingclaims.

What is claimed is:
 1. A method of modeling to simulate potential forpower and energy savings in a data center, comprising: selecting athreshold CPU utilization rate; collecting, in data collection intervalsof a specified time duration, CPU utilization rate data from a group ofcomputing machines; determining that a collective CPU utilization rateof the group of computing machines falls below the threshold CPUutilization rate; for each computing machine having a CPU component inthe group of computing machines that falls below the threshold CPUutilization rate, reducing the CPU component of total power by using aCPU voltage and frequency scaling (DVS) simulation; and maintaining apower consumption of one or more components of the computing machineother than the CPU component unchanged.
 2. The method of claim 1,wherein DVS simulation comprises reducing the CPU component of totalpower by about half.
 3. The method of claim 1, wherein using DVSsimulation comprises: determining an application executing on thecomputing machine that falls below the threshold CPU utilization rate;and based on the application meeting a particular criteria, activatingDVS simulation.
 4. The method of claim 3, wherein the applicationcomprises one of a search request application, an email application, amap request application, or a mix of applications.
 5. The method ofclaim 3, further comprising, after using DVS simulation, performing adetailed application characterization to determine a level ofperformance of the computing machine, the detailed applicationcharacterization comprising determining a rate of reduction in peakpower of the application and determining a rate of energy saving of theapplication based on the threshold CPU utilization rate.
 6. The methodof claim 5, wherein performing the detailed application characterizationcomprises determining 1) the rate of reduction in peak power and 2) therate of energy saving of a search request application, an emailapplication, and a map request application.
 7. The method of claim 5,wherein performing the detailed application characterization comprisesdetermining 1) the rate of reduction in peak power and 2) the rate ofenergy saving of the application based on a threshold CPU utilizationrate of 5%, a threshold CPU utilization rate of 20%, and a threshold CPUutilization rate of 50%.
 8. The method of claim 1, wherein the thresholdCPU utilization rate comprises one of 5%, 20%, or 50%.
 9. The method ofclaim 1, wherein the data collection intervals are intervals of 10minutes.
 10. The method of claim 9, wherein the group of computingmachines comprises a cluster of data center computing machines, andwherein the collective CPU utilization rate comprises an average CPUutilization rate of the computing machines in the cluster of data centercomputing machines.
 11. The method of claim 1, wherein the group ofcomputing machines comprises a cluster of data center computingmachines, and wherein the collective CPU utilization rate comprises anaverage CPU utilization rate of the computing machines in the cluster ofdata center computing machines.
 12. A method of modeling non-peak powerefficiency, comprising: calculating, in a computer, a power draw for acomputer as a linear function of CPU utilization rate, wherein an offsetand a slope of the linear function are based at least in part on one ormore components of an application of the computer.
 13. The method ofclaim 12, wherein calculating the power draw includes setting an idlepower of the computer at about 10% of actual peak power.
 14. The methodof claim 13, wherein calculating the power draw includes scaling powerdraw for an utilization greater than idle as proportional to theincrease in utilization.
 15. The method of claim 12, further comprisingcalculating, in a computer, a power draw for a plurality of computers,including setting an idle power of each machine in the plurality ofmachines at about 10% of actual peak power, and scaling power draw foreach machine in the plurality of machines proportional to increasedactivity.
 16. The method of claim 15, wherein the linear functioncomprises a linear regression.
 17. The method of claim 16, wherein thelinear regression comprises a curve fit to CPU utilization for aplurality of computers in a data center.
 18. The method of claim 15,wherein the plurality of computers comprises a cluster of logicallyconnected data center computing machines.
 19. The method of claim 12,further comprising maintaining all other parameters, including theactual peak power, unchanged.
 20. The method of claim 19, wherein theplurality of computers comprises a cluster of logically connected datacenter computing machines.